The operating mode of an electric vehicle is often characterized by its time until recharge and the options surrounding this. Existing methods of power management to optimize the electric-drive mode include rule based control algorithms, based on static variables, and pattern recognition which can include current driving patterns such as acceleration, regenerative-braking energy and stop time.
Dynamic programming algorithms have been used, based on the knowledge of real-time data to model a trip. Simulations have included intelligent traffic systems and global positioning systems technologies in analytical models. Real-time traffic data can be analyzed to optimized operations and have been used in geographic scaling to localize the inputs in order to attempt to reduce computational requirements. Vehicle power management systems can include real-time traffic data such as traffic flow, traffic light stop time, traffic congestion and so on in a predictive energy management system.
Efforts to improve performance have included the use of traffic data including traffic flow and intersection light status to determine if alternative routes should be considered; as well as, vehicle operation modes including vehicle speed, transmission gear and state of charge. Trip modeling has included road conditions such as slope and distance, incorporating global positioning systems and re-routing following the processing of traffic data.
Boll et al. (U.S. Pat. No. 5,790,976), Boss et al. (U.S. Pat. No. 8,014,914 B2), Woestman, et al. (U.S. Pat. No. 6,487,477), and Shunder (U.S. Pat. No. 8,185,302 B2) disclose such on-board energy management routing systems.
Boll et al. (U.S. Pat. No. 5,790,976) discloses an on-board route selecting device for vehicles with a prescribed capacity of energy storage that takes into account energy supply/refuelling/recharging locations along possible routes. Boss et al. (U.S. Pat. No. 8,014,914 B2) discloses methods of route planning based on the vehicle type of either an internal combustion engine or hybrid.
Woestman, et al. (U.S. Pat. No. 6,487,477) discloses the integration of an on-board navigation system with the energy management control system for an electric vehicle (EV) and a hybrid electric vehicle (HEV). The vehicle's real-time location is continuously monitored, expectations of driver demand are determined, and vehicle accommodations are made. The system can be configured to include as part of its present vehicle location data on road patterns, geography with date and time, altitude changes, speed limits, driving patterns of a vehicle driver, and weather. The vehicle accommodations can be configured to use discrete control laws, fuzzy logic, or neural networks.
Shunder (U.S. Pat. No. 8,185,302 B2) discloses a method of determining an optimal fuel usage route using a vehicle based computing system which uses data received through a GPS unit or a wireless telematic device either installed in the vehicle or portable device such as a cell phone. The system aggregates weighted values of possible routes taking into account road related data such as distance, braking, speed constancy, energy generation and projected fuel consumption. Based on adjusted or weighted values the system suggests an optimal route.
However, electric vehicle systems are multi variable dynamic systems and there are high computational loads associated with integrating real-time traffic data with the energy management systems of these vehicles. Additionally, operators are becoming increasingly more connected with informational networks, and their expectations are increasing for adaptive mobile vehicle systems that can intelligently manage and integrate the various vehicle operational patterns and systems, driver patterns and preferences, along with the massive amounts of real-time, historical and predictive geo spatial data being generated to provide an optimal driving experience. Use of such a system will result in higher electric vehicle performance thereby becoming more widely accepted in comparison to internal combustion engines.
The present art systems are limited to the processing of vehicle centric real-time information in combination with relative geospatial information. This cannot presently be efficiently managed, and this limits the ability of systems to provide predictive analytics. This is further restricted by the ability to manage vast numbers of distributed inputs on an ongoing basis. Therefore there is a need to provide improved mobile telematics and navigation and improved means for managing such information.
This background information is provided to reveal information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.